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Quantifying international human mobility patterns using Facebook Network data

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  • Spyridon Spyratos
  • Michele Vespe
  • Fabrizio Natale
  • Ingmar Weber
  • Emilio Zagheni
  • Marzia Rango

Abstract

Quantifying global international mobility patterns can improve migration governance. Despite decades of calls by the international community to improve international migration statistics, the availability of timely and disaggregated data about long-term and short-term migration at the global level is still very limited. In this study, we investigate the feasibility of using non-traditional data sources to fill existing gaps in migration statistics. To this end, we use anonymised and publicly available data provided by Facebook’s advertising platform. Facebook’s advertising platform classifies its users as “lived in country X” if they previously lived in country X, and now live in a different country. Drawing on statistics about Facebook Network users (Facebook, Instagram, Messenger, and the Audience Network) who have lived abroad and applying a sample bias correction method, we estimate the number of Facebook Network (FN) “migrants” in 119 countries of residence and in two time periods by age, gender, and country of previous residence. The correction method estimates the probability of a person being a FN user based on age, sex, and country of current and previous residence. We further estimate the correlation between FN-derived migration estimates and reference official migration statistics. By comparing FN-derived migration estimates in two different time periods, January-February and August-September 2018, we successfully capture the increase in Venezuelan migrants in Colombia and Spain in 2018. FN-derived migration estimates cannot replace official migration statistics, as they are not representative, and the exact methods the FN uses for classifying its users are not known, and might change over time. However, after carefully assessing the validity of the FN-derived estimates by comparing them with data from reliable sources, we conclude that these estimates can be used for trend analysis and early-warning purposes.

Suggested Citation

  • Spyridon Spyratos & Michele Vespe & Fabrizio Natale & Ingmar Weber & Emilio Zagheni & Marzia Rango, 2019. "Quantifying international human mobility patterns using Facebook Network data," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-22, October.
    • Handle: RePEc:plo:pone00:0224134
    DOI: 10.1371/journal.pone.0224134
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    References listed on IDEAS

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    Cited by:

    1. Luis E C Rocha & Petter Holme & Claudio D G Linhares, 2022. "The global migration network of sex-workers," Journal of Computational Social Science, Springer, vol. 5(1), pages 969-985, May.
    2. Carolina Coimbra Vieira & Sophie Lohmann & Emilio Zagheni, 2023. "The value of cultural similarity for predicting migration: evidence from digital trace data," MPIDR Working Papers WP-2023-009, Max Planck Institute for Demographic Research, Rostock, Germany.
    3. Tatsuro Kawamoto & Ryutaro Hashimoto, 2021. "Identifying macroscopic features in foreign visitor travel pathways," The Japanese Economic Review, Springer, vol. 72(1), pages 129-144, January.
    4. Bert Leysen & Pieter-Paul Verhaeghe, 2023. "Searching for migration: estimating Japanese migration to Europe with Google Trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4603-4631, October.
    5. Daniela Perrotta & Sarah C. Johnson & Tom Theile & André Grow & Helga de Valk & Emilio Zagheni, 2022. "Openness to migrate internationally for a job: evidence from LinkedIn data in Europe," MPIDR Working Papers WP-2022-007, Max Planck Institute for Demographic Research, Rostock, Germany.
    6. Barbara Brollo & Filippo Celata, 2023. "Temporary populations and sociospatial polarisation in the short-term city," Urban Studies, Urban Studies Journal Limited, vol. 60(10), pages 1815-1832, August.

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